Services

Loan Loss Database (LGD and EAD pooling)

Global Credit Data's initial focus has been to collect data to assist with the measurement of Loss Given Default (LGD) and the Exposure At Default (EAD). A template and process has been developed over the years to ensure that the necessary information for the calculation of these parameters is being collected. Data related to credit failures (default) dating back to 1998 is being submitted by Member-banks, allowing for meaningful statistics in terms of type of borrower, time and size of exposure at default and collateral recovery rates becoming available to the contributors for their own modelling.

Data is collected and analysed separately for each of the following Asset Classes:

Large corporates,

Banks,

SME,

Aircraft finance,

Ship finance,

Project finance,

Real Estate finance,

Commodities finance,

Public services,

Sovereign,

Private banking.

The same database is used to support analysis of EAD including parameters which support the various definitions of credit conversion factors (CCF).

In 2009 Global Credit Data started a study of Observed Default Frequencies, i.e. the number of defaults actually counted by a bank in a year within a segment of its obligors. This exercise complements the analyses on LGDs and EADs, and, like them, is strictly historical. In 2015, the data template and process has been amended in such a way that also migration matrices and multi-year defaults rates (both relevant for stresstesting and IFRS 9 impairment modelling) are calculated and given back to the participating banks.

The database input for this datapooling is much simpler than the LGD-EAD database: we are simply asking for quarterly "portfolio snapshots", including basic information such as asset class, country/geographic region, industry, rating category and PD. All this enables us to calculate and give back default rates, migration rates and average PDs for a vast amount of different segments - information which member banks can use instantly to benchmark their internal PD systems.

Since our last collection exercise our database contains more than 1.5 million obligors and 84,000 defaults through the last economic cycle (from 2002 until 2015) from 24 banks, split out by year, rating, region, industry and asset class. This database allows us peer-benchmarking of internal rating models, construction of correlation matrices and to backtest the KIRB formula of Basel 2 based on real Basel 2 defaults.

Benchmarking Platform (peer comparison of risk estimates)

Coming in 2017...Benchmarking of current predicted PD, EAD/CCF and LGD for named counerparties and specific banking book clusters.

Model Benchmarking activities

Given the maturity of models for credit risk at many of our member banks, Global Credit Data has in recent years focussed more on studies, surveys and working group activities intended to allow members to benchmark their credit risk models and drivers.

On our own and in conjunction with financial industry bodies such as The Institute of International Finance (IIF), Global Credit data conducts surveys and performs comparisons and analyses to help members better understand their own memthods and estimates.

We have also conducted on two occasions a collection of the Hypothetical Portfolio Estimate studies run by the European Banking Authority and the Basel Committee for Banking Supervision. The collection and return of hypothetical PD and LGD estimates for named counterparties gives our members the ability to better understand the regulators' views on RWA diversity and to discuss their banks position with management and regulators.